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Shop Floor Analysis Using R for Decision Making To Enhance Productivity and Improve Quality

Published: 13 May 2024 Publication History

Abstract

Shop floor control is often challenging due to several methodologies being adopted and techniques being implemented for accomplishing high productivity and good quality. In order to plan and execute strategies for continuous improvement, complete information about the production system is essential. As the study and analysis of all information with respect to sourcing, manufacturing and distribution are must and mandatory for efficient operations management data analysis with regards to production helps for an effective shop floor control. In this regard, the article presented here conducts an organized Quantitative analysis considering wastages, defectives, breakdowns, idle time and production quantity resulting in recommendations that will enhance the Quality, Quantity and enabling Longevity of the machines. The shop floor analysis is carried out using the statistical language R and the outcomes are validated. The findings of the shop floor analysis presented in this paper are much helpful for corporate decision making for accomplishing enhanced productivity and improved quality.

References

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M.Müller, L.Longard, J.Metternich, “Comparison of preprocessing approaches for text data in digital shop floor management systems”, Procedia, CIRP, Volume 107, Pp 179-184, 2022
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Young -Jun Son, “Simulation based shop floor control”, Journal of Manufacturing Systems, volume 21, Issue 5, PP 380-394, 2001
[3]
Ou-Yang, C., Chang, R. Applying an Integrated Analysis Method to Develop a Shop Floor Control System. Int J Adv Manuf Technol 16, 353–369 (2000). https://doi.org/10.1007/s001700050167
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Mbakop, A.M., Voufo, J., Biyeme, F. et al. Analysis of Information Flow Characteristics in Shop Floor: State-of-the-Art and Future Research Directions for Developing Countries. Glob J Flex Syst Manag 22, 43–53 (2021). https://doi.org/10.1007/s40171-020-00257-3
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Scherer, E. (1998). The Reality of Shop Floor Control — Approaches to Systems Innovation. In: Scherer, E. (eds) Shop Floor Control — A Systems Perspective. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60313-6_1
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Yadan, Arun Singh (2023). A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor Classification. Journal of Imaging. https://doi.org/10.3390/jimaging9010010

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ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
November 2023
1215 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2024

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Author Tags

  1. Manufacturing
  2. Quantity
  3. Shop Floor Control
  4. Sourcing
  5. and Distribution

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